Readme
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Recommended hardware environment:
SWAP space >= 64GB
VRAM >= 8GB
Ram >= 32GB
hard drive space >= 80GB
Recommended software environment:
GPU driver: nvidia-driver-450 or nvidia-driver-460
CUDA driver: V10.1
Anaconda python-3.7
Training semantic CNN could direct import conda env semantic_segmentation.yaml
Training DeepLabCut could direct import conda env DLC.yaml
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Neural network training guide:
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Clone repository
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Download data from the link: https://drive.google.com/file/d/1xiVPbgRNmHnDyBE8pZbU0m_WMcuC1Kf8/view?usp=sharing
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Extract the data, move the images in folder "images" to the repository folder "Microtubules_Detection-master/Semantic_Segmentation/training_data/images", move the labels in folder "labels" to the repository folder "Microtubules_Detection-master/Semantic_Segmentation/training_data/labels".
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Run the script Divide_Data_for_Training_and_Testing.py in folder "Microtubules_Detection-master/Semantic_Segmentation/training_data/"
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Run the script Augmentation.py in folder "Microtubules_Detection-master/Semantic_Segmentation/training_data/".
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Run the script Neural_Network_Training.py in folder "Microtubules_Detection-master/Semantic_Segmentation/".
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To implement into new sequence after model is trained:
(If needed)Trained network model download link: https://drive.google.com/file/d/1DcdysUrOZF6n4mP0bd7U7637yIShauK7/view?usp=sharing (Make sure the trained network("MT_1216_Semantic_Segmentation.h5") is in the folder "Microtubules_Detection-master/Semantic_Segmentation/")
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Backup the last implemented data, run Delete_Last_Implementation_Data.py in folder "Microtubules_Detection-master/Semantic_Segmentation/implementation/"
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Put ND2 video into folder "Microtubules_Detection-master/Data_Process/ND2_Video/", and seeds ND2 file into folder "Microtubules_Detection-master/Data_Process/ND2_Seeds/".
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Run ND2_File_Decomposition.py in folder "Microtubules_Detection-master/Data_Process/". (Contrast and brightness are adjustable depand on the input data)
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Run Duplicate_Data_to_Implementation_Input_Folder.py in folder "Microtubules_Detection-master/Data_Process/"
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Run Implementation_Prediction.py in folder "Microtubules_Detection-master/Semantic_Segmentation/implementation/"
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Run Length_Measurement.py in folder "Microtubules_Detection-master/Semantic_Segmentation/implementation/"
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Run Automatic_Linear_Regression.py in folder "Microtubules_Detection-master/Semantic_Segmentation/implementation/"
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(Optional) Run Manually_Linear_Regression_Correction.py in folder "Microtubules_Detection-master/Semantic_Segmentation/implementation/" if the automatic detected linear regressions are inaccurate.
All the output data is in the folder "Microtubules_Detection-master/Semantic_Segmentation/implementation/data_output"
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The json file to dataset shell command is included in repository(json_to_dataset.sh). To transfer json file to image data, use console cd to the file directory, further run shell command $sh json_to_dataset.sh